DeepSeek's October 2026 agentic flagship with refusal behavior removed in the weights — 1M context, vision, tools, and reasoning tiers at a fraction of closed-model pricing.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-1-flash-1017-uncensored",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Overview
DeepSeek V4.1 Flash 1017 Uncensored is an abliterated build of DeepSeek's October 17, 2026 V4.1 Flash model — a large mixture-of-experts LLM with 1M-token context, vision, tool calling and reasoning-effort tiers — with refusal behavior removed directly in the weights by Dealign AI. It ships under the MIT licence and runs privately on Venice.
On Venice, this model runs under the private tier with zero retention: your prompts are not stored, profiled, or used for training, and there is no personal history tied to your account. Because the uncensoring is baked into the weights rather than a jailbreak prompt, it behaves the same on every request. Venice serves the native FP8 checkpoint, and per-token billing means you only pay for what you use — no subscription required.
Agent quickstart
The API is OpenAI-compatible: change the base URL and the model id and existing client code works unchanged.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-1-flash-1017-uncensored",
"stream": true,
"messages": [{ "role": "user", "content": "Draft the release note." }]
}'curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4-1-flash-1017-uncensored",
"messages": [{ "role": "user", "content": "Find the rate limits." }],
"tools": [{
"type": "function",
"function": {
"name": "search_docs",
"description": "Search the API documentation.",
"parameters": {
"type": "object",
"properties": { "query": { "type": "string" } },
"required": ["query"]
}
}
}],
"tool_choice": "auto"
}'import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
resp = client.chat.completions.create(
model="deepseek-v4-1-flash-1017-uncensored",
messages=[{"role": "user", "content": "Build without permission."}],
)
print(resp.choices[0].message.content)Specifications
Assessment
Use cases
Prompting
Set the reasoning effort explicitly: low for quick answers, max for hard agentic or math work — the publisher's card lists low/high/max tiers.
Use the publisher's recommended sampling: temperature 1.0 and top_p 1.0, with max_tokens well above your expected output for heavy reasoning runs.
Attach multiple images in one request (logs, screenshots, diagrams) instead of spreading them across turns — multi-image input is supported.
Request JSON-schema structured output when chaining the model into tools, so function-call arguments arrive parseable.
Stuff your whole repo or document set into the 1M-token context for code tasks rather than summarizing — the model is tuned for large-context agentic work.
Turn thinking off for simple prompts — third-party testing showed it collapses reasoning output from ~43 tokens to ~2, cutting latency and cost.
Alternatives
| Model | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| DeepSeek V4.1 Flash 1017 Uncensored | Agentic coding & low-refusal work | 1M tokens | Yes | $0.33 in · $1.32 out / 1M |
| Gemini 3.8 Flash | Fast general assistant | 1M tokens | No | $0.94 in · $4.69 out / 1M |
| Kimi K3.1 Pro | Long-document reasoning | 1M tokens | No | $3 in · $15 out / 1M |
| Qwen 3.7 Plus | Balanced multilingual chat | 1M tokens | No | $0.50 in · $2 out / 1M |
| Google Gemma 4 31B Instruct | Cheap open-weight fallback | 256K tokens | Yes | $0.12 in · $0.36 out / 1M |
Pick DeepSeek V4.1 Flash 1017 Uncensored when you need an open-weight, tool-using coding agent that keeps going where stock models refuse — at a third of Gemini's price and a tenth of Kimi's. For closed-model polish on general chat, Gemini 3.8 Flash is the alternative.
Pricing
Billed per token on Venice: $0.33 per 1M input tokens and $1.32 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Getting a key
Create a key in API settings. Nothing else is required to start.
Export it as VENICE_API_KEY so the snippets above run unedited.
Point an existing OpenAI client at https://api.venice.ai/api/v1. The scheme is part of the value: an OpenAI client given a bare host does not resolve it.
Pass deepseek-v4-1-flash-1017-uncensored as the model and send the request.
FAQ
It is an abliterated version of DeepSeek-V4.1-Flash-1017, DeepSeek's October 17, 2026 release, with its refusal behavior removed in the weights by Dealign AI. It keeps the base model's 1M-token context, vision input, tool calling, reasoning-effort tiers, and MIT licence, and is served on Venice in native FP8.
Venice bills per token: $0.33 per 1M input tokens, $1.32 per 1M output tokens, and $0.01 per 1M cached input tokens. There is no subscription required — you pay only for what you use.
The underlying model is open source under the MIT licence — DeepSeek published the base weights on Hugging Face, and Dealign AI published the uncensored FP8 checkpoint. Using it on Venice is paid per token, though new Venice accounts include free daily prompts and welcome credits to try it.
It means the refusal behavior was removed at the weight level through abliteration — not via a system prompt, LoRA adapter, or runtime hook. The publisher reports the model answers across topic categories where the stock model declines, while preserving vision, tools, reasoning tiers, and the 1M context.
Yes. On Venice it supports tool use / function calling, vision with multiple image inputs per request, reasoning with selectable effort tiers, web search, structured JSON-schema output, and code-optimized tuning — all the upstream capabilities are preserved in the uncensored build.
Choose DeepSeek for agentic coding, large-context analysis, and workloads where stock models refuse mid-task — it is open weight and roughly a third of Gemini's price on Venice. Choose Gemini 3.8 Flash for fast, closed-model general assistance where raw polish matters more than openness or price.
It runs in Venice's private tier with zero retention: prompts are not stored, profiled, or used for training. Note that this model is not served inside a TEE and does not use end-to-end encryption — the privacy guarantee is Venice's no-storage policy, not cryptographic isolation.
The publisher's own testing reports MMLU dropping from 87.0% to 82.7%, with the loss concentrated in moral-scenario and professional-law questions. General reasoning, coding, and agentic benchmarks were preserved, and the edit was applied to native FP8 weights to minimize quantization error.
The weights are public on Hugging Face under the MIT licence, so self-hosting is legally fine — but at 552B MoE parameters in FP8, independent testers needed four H200 GPUs. Most users will find Venice's per-token pricing far cheaper than running the hardware.
One key, free to start, no credit card.